{"id":"W4402031318","doi":"10.1021/acssensors.4c00806","title":"Class-Wide Analysis of Frizzled-Dishevelled Interactions Using BRET Biosensors Reveals Functional Differences among Receptor Paralogs","year":2024,"lang":"en","type":"article","venue":"ACS Sensors","topic":"Receptor Mechanisms and Signaling","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Innovative Medicines Initiative; Horizon 2020 Framework Programme; Novo Nordisk Fonden; Vetenskapsrådet; Cancerfonden; Karolinska Institutet; European Commission; Kungliga Tekniska Högskolan; Deutsche Forschungsgemeinschaft; Diamond Light Source; Novo Nordisk; European Federation of Pharmaceutical Industries and Associations; McGill University","keywords":"Dishevelled; Frizzled; Computational biology; Biology; Biosensor; Receptor; Class (philosophy); Evolutionary biology; Neuroscience; Cell biology; Genetics; Signal transduction; Computer science; Biochemistry; Wnt signaling pathway; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002772303,0.0003454511,0.0003745029,0.0005471929,0.000266438,0.0007335316,0.0003692297,0.0005444802,0.002166132],"category_scores_gemma":[0.0002730601,0.0001637561,0.0003117509,0.000354281,0.0003357809,0.0002754282,0.000350408,0.0006091413,0.0008551776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005942055,"about_ca_system_score_gemma":0.0001611928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001043926,"about_ca_topic_score_gemma":0.002039271,"domain_scores_codex":[0.9995747,0.00002271567,0.00001772371,0.000157651,0.0001540582,0.00007304191],"domain_scores_gemma":[0.9998609,0.00003369984,0.00004197306,0.00002371727,0.00001851907,0.00002123961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003165249,0.000008043142,0.0003304855,0.00002051664,0.000008128667,0.00001072876,0.000008160435,0.00002576153,0.9978064,0.00003939772,0.00003603349,0.001674826],"study_design_scores_gemma":[0.00000322059,0.00004658712,0.009877706,0.000002153518,0.00001439408,0.0001390815,0.00002291738,0.0005750417,0.9874214,0.00004895143,0.00184159,0.00000694455],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9683339,0.00326738,0.02283083,0.0001869715,0.00003472758,0.00005261933,0.002013016,0.0003989882,0.002881638],"genre_scores_gemma":[0.9847014,0.00126586,0.007531704,0.0001738248,0.000008251071,0.00007328293,0.001901036,0.00007473272,0.004269895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002166132,"threshold_uncertainty_score":0.007246375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02475544220423765,"score_gpt":0.2658977820995616,"score_spread":0.2411423398953239,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}